---
sidebar_position: 5
---
You can subscribe to these events by using the `callbacks` argument available throughout the API. This argument is list of handler objects, which are expected to implement one or more of the methods described below in more detail.

## Callback handlers

`CallbackHandlers` are objects that implement the `CallbackHandler` interface, which has a method for each event that can be subscribed to. The `CallbackManager` will call the appropriate method on each handler when the event is triggered.

```python
class BaseCallbackHandler:
    """Base callback handler that can be used to handle callbacks from langchain."""

    def on_llm_start(
        self, serialized: Dict[str, Any], prompts: List[str], **kwargs: Any
    ) -> Any:
        """Run when LLM starts running."""

    def on_chat_model_start(
        self, serialized: Dict[str, Any], messages: List[List[BaseMessage]], **kwargs: Any
    ) -> Any:
        """Run when Chat Model starts running."""

    def on_llm_new_token(self, token: str, **kwargs: Any) -> Any:
        """Run on new LLM token. Only available when streaming is enabled."""

    def on_llm_end(self, response: LLMResult, **kwargs: Any) -> Any:
        """Run when LLM ends running."""

    def on_llm_error(
        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
    ) -> Any:
        """Run when LLM errors."""

    def on_chain_start(
        self, serialized: Dict[str, Any], inputs: Dict[str, Any], **kwargs: Any
    ) -> Any:
        """Run when chain starts running."""

    def on_chain_end(self, outputs: Dict[str, Any], **kwargs: Any) -> Any:
        """Run when chain ends running."""

    def on_chain_error(
        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
    ) -> Any:
        """Run when chain errors."""

    def on_tool_start(
        self, serialized: Dict[str, Any], input_str: str, **kwargs: Any
    ) -> Any:
        """Run when tool starts running."""

    def on_tool_end(self, output: str, **kwargs: Any) -> Any:
        """Run when tool ends running."""

    def on_tool_error(
        self, error: Union[Exception, KeyboardInterrupt], **kwargs: Any
    ) -> Any:
        """Run when tool errors."""

    def on_text(self, text: str, **kwargs: Any) -> Any:
        """Run on arbitrary text."""

    def on_agent_action(self, action: AgentAction, **kwargs: Any) -> Any:
        """Run on agent action."""

    def on_agent_finish(self, finish: AgentFinish, **kwargs: Any) -> Any:
        """Run on agent end."""
```

## Get started

LangChain provides a few built-in handlers that you can use to get started. These are available in the `langchain/callbacks` module. The most basic handler is the `StdOutCallbackHandler`, which simply logs all events to `stdout`.

**Note**: when the `verbose` flag on the object is set to true, the `StdOutCallbackHandler` will be invoked even without being explicitly passed in.

```python
from langchain.callbacks import StdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate

handler = StdOutCallbackHandler()
llm = OpenAI()
prompt = PromptTemplate.from_template("1 + {number} = ")

# Constructor callback: First, let's explicitly set the StdOutCallbackHandler when initializing our chain
chain = LLMChain(llm=llm, prompt=prompt, callbacks=[handler])
chain.run(number=2)

# Use verbose flag: Then, let's use the `verbose` flag to achieve the same result
chain = LLMChain(llm=llm, prompt=prompt, verbose=True)
chain.run(number=2)

# Request callbacks: Finally, let's use the request `callbacks` to achieve the same result
chain = LLMChain(llm=llm, prompt=prompt)
chain.run(number=2, callbacks=[handler])
```

<CodeOutputBlock lang="python">

```
    > Entering new LLMChain chain...
    Prompt after formatting:
    1 + 2 = 
    
    > Finished chain.
    
    
    > Entering new LLMChain chain...
    Prompt after formatting:
    1 + 2 = 
    
    > Finished chain.
    
    
    > Entering new LLMChain chain...
    Prompt after formatting:
    1 + 2 = 
    
    > Finished chain.


    '\n\n3'
```

</CodeOutputBlock>

## Where to pass in callbacks

The `callbacks` argument is available on most objects throughout the API (Chains, Models, Tools, Agents, etc.) in two different places:

- **Constructor callbacks**: defined in the constructor, e.g. `LLMChain(callbacks=[handler], tags=['a-tag'])`, which will be used for all calls made on that object, and will be scoped to that object only, e.g. if you pass a handler to the `LLMChain` constructor, it will not be used by the Model attached to that chain.
- **Request callbacks**: defined in the `run()`/`apply()` methods used for issuing a request, e.g. `chain.run(input, callbacks=[handler])`, which will be used for that specific request only, and all sub-requests that it contains (e.g. a call to an LLMChain triggers a call to a Model, which uses the same handler passed in the `call()` method).

The `verbose` argument is available on most objects throughout the API (Chains, Models, Tools, Agents, etc.) as a constructor argument, e.g. `LLMChain(verbose=True)`, and it is equivalent to passing a `ConsoleCallbackHandler` to the `callbacks` argument of that object and all child objects. This is useful for debugging, as it will log all events to the console.

### When do you want to use each of these?

- Constructor callbacks are most useful for use cases such as logging, monitoring, etc., which are _not specific to a single request_, but rather to the entire chain. For example, if you want to log all the requests made to an `LLMChain`, you would pass a handler to the constructor.
- Request callbacks are most useful for use cases such as streaming, where you want to stream the output of a single request to a specific websocket connection, or other similar use cases. For example, if you want to stream the output of a single request to a websocket, you would pass a handler to the `call()` method

